Data Scientist

Experteer Overview In this hands-on role, you will build, test, and maintain ML models to power demand forecasting, store analytics, and feature engineering for ongoing model improvement. You will work on a small, high-output team under the guidance of the Manager of Data Engineering, AI & ML, shaping data-driven decisions across retail domains. Your work spans forecasting, feature store enrichment, backtesting, and interpretation of model results to inform promotions and operations. This is a hybrid, in-office role based in River North, Chicago, offering scope to influence GTI’s analytics stack and AI ecosystem. Compensation / Benefits * Build, validate, and refine demand forecasting models for GTI’s retail, wholesale, and other verticals across multiple horizons * Engineer features for the Snowflake Feature Store from diverse data sources to boost model accuracy * Develop and backtest model candidates using established frameworks and present findings for decision-making * Investigate forecasting errors and data drift to diagnose causes and propose remediation * Perform dimensionality reduction and PCA to understand key feature importance * Collaborate on evolving the feature engineering roadmap and signal generation * Design analytical studies and reusable frameworks to answer business questions * Translate findings into clear summaries and visuals for non-technical stakeholders * Contribute to team roadmap discussions and familiarize with GTI’s data stack (Snowflake, dbt, Dagster) Tasks * 2+ years in data science, quantitative analysis, or ML engineering with hands-on modeling or feature engineering * Strong Python skills (pandas, scikit-learn, statsmodels) and Jupyter/Notebook experience * Strong SQL: complex queries, multi-grain aggregations, data quality validation * Experience with supervised/unsupervised ML (gradient boosting, time series, random forest) * Ability to clearly communicate analytical findings and actionable insights * Intellectual curiosity and bias toward solving real-world data problems Key requirements * hybrid work model * competitive pay range $90,000 - $115,000 USD * discretionary annual incentive program * growth opportunities within AI/ML initiatives * collaborative team environment

of forecasting errors and data drift to diagnose causes and propose remediation * Perform dimensionality reduction and PCA to understand key feature importance * Collaborate on evolving the feature engineering roadmap and signal generation * Design analytical studies and reusable frameworks to answer business questions * Translate findings into clear summaries and visuals for non-technical stakeholders * Contribute to team roadmap discussions and familiarize with GTI’s data stack (Snowflake, dbt, Dagster) Tasks * 2+ years in data science, quantitative analysis, or ML engineering with hands-on modeling or feature engineering * Strong Python skills (pandas, scikit-learn, statsmodels) and Jupyter/Notebook experience * Strong SQL: complex queries, multi-grain aggregations, data quality validation * Experience with supervised/unsupervised ML (gradient boosting, time series, random forest) * Ability to clearly communicate analytical findings and actionable insights * Intellectual curiosity and bias toward solving real-world data problems Key requirements * hybrid work model * competitive pay range $90,000 - $115,000 USD * discretionary annual incentive program * growth opportunities within AI/ML initiatives * collaborative team environment